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On Rejecting Low Quality Images to Improve Deep Smartphone Wound Assessment 22

On Rejecting Low Quality Images to Improve Deep Smartphone W...

引用

22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Ditthapron, Apiwat Agu, Emmanuel O. Pedersen, Peder Lindsay, Clifford Tulu, Bengisu Strong, Diane Computer Science Dept. WorcesterMA United States Electrical and Computer Engineering Dept. WorcesterMA United States Univ. Massachusetts Medical School Department of Radiology WorcesterMA United States WorcesterMA United States

ISBN: (纸本)9798350345346

Automatic assessment of wounds using smartphones can facilitate the tracking of healing progress outside the clinic. Prior work proposed SS-PMG-EfficientNets, a state-of-the-art, multi-scale, convolutional neural network architecture for accurate wound assessment. However, the model was previously only trained and evaluated on high-quality wound images captured in controlled environments. This study systematically evaluated the effects of low-quality images on SS-PMG-EfficientNets, finding that the model's performance and consistency are reduced when input wound images are blurry or captured in adverse lighting (too bright or too dark), two factors previously identified as the most prevalent issues in crowdsourced images. Specifically, SS-PMG-EfficientNet was first trained on three high-quality wound datasets containing images labeled with Photographic Wound Assessment Tool (PWAT) wound healing scores, stages of pressure injury wounds, and wound types, respectively. The model was then evaluated on a test wound dataset that was augmented with real-world image luminance, blur, and image compression effects and found that wound assessment accuracy decreased by up to 27%. To improve model performance, we propose a method for instant image quality assessment on the smartphone, enabling low-quality wound images to be rejected immediately after image capture so that the nurse or patient can be prompted to recapture the image. Our evaluation of the proposed image quality assessment and rejection method showed that it increases average accuracy from 73 % to 87%. © 2023 IEEE.

关键词： Chronic wounds Image quality assessment Smart health

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Vector Model Based Information Retrieval System with Word Embedding Transformation 10

Vector Model Based Information Retrieval System with Word Em...

引用

10th International Conference on Emerging Trends in engineering and Technology - Signal and Information Processing, ICETET-SIP-22 2022

作者： Brundha, J. Meera, K.N. Amrita School of Engineering Dept. of Computer Science and Engineering Bengaluru India Amrita School of Engineering Dept. of Mathematics Bengaluru India

ISBN: (纸本)9781665467414

Vector based information retrieval system has been one of the trending methods in Natural Language Processing. The embeddings vector generated from a document helps in identifying most relevant document related to the query. There is various approach were embedding vectors can be generated and some of them which have implemented are Word2vec, Glove2vec and Sentence BERT. For information retrieval system also used word embedding transformation like PCA and Factor Analysis to improvise the model's performance. Most of information retrieval system involves getting query from the user, preprocessing of the query and generating most relevant information to the query. Results obtained by post processing methods such as PCA and Factor Analysis shows a comparatively better results with an increase of 2-3% of Mean average precision. © 2022 IEEE.

关键词： Analytical models Machine learning algorithms Bit error rate Machine learning Information processing Euclidean distance Information retrieval

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An Effective IOT based Driver's Drowsiness Detection and Monitoring System to Avoid Real-Time Road Accidents 3

An Effective IOT based Driver's Drowsiness Detection and Mon...

引用

3rd IEEE Global Conference for Advancement in Technology, GCAT 2022

作者： Qureshi, Neha Chaudhari, Shilpa Kallimani, Jagadish S. Ramaiah Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (数字)9781665468558

ISBN: (纸本)9781665468534

Many people know that driving while drowsy is dangerous, but they may not know how to detect their own level of sleepiness. The proposed solution will provide you with information on how to look for signs of potential driver drowsiness and take appropriate action. The existing techniques are classified into three major classes namely: vehicular based, behavioral based and physiological based methods. The vehicular based methods utilize parameters like steering control, brake control, acceleration on the pedal and lane deviations. The behavioral based methods utilize parameters like eye blinking, mouth ratio for yawning and head pose. The physiological based methods utilize parameters like heart rate activity, muscle activity and brain activity. These systems are designed to monitor the driver's sleepy state and alert him for prevention of road accidents. However, the accuracy for detecting sleepiness is very low and hence the proposed system will combine these methods to introduce a hybrid system to improve the detection rate. A buzzer is placed to alert the driver when the sleepiness is detected. This would allow the driver to rest or have a coffee instead of driving for long periods without realizing that he is too tired. The proposed solution will also detect the collision of the vehicle and send the location information of the driver through a SMS to the vehicle owner, in case if he is not awakened by the buzzer and met with any minor accident. © 2022 IEEE.

关键词： Brain

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Performance assessment of supercapacitor energy storage integration into a renewable DC microgrid 2

Performance assessment of supercapacitor energy storage inte...

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2nd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2022

作者： Papageorgiou, Pavlos G. Tsakiri, Anna M. Christoforidis, Georgios C. University of Western Macedonia Dept. of Electrical & Computer Engineering Kozani Greece

ISBN: (数字)9781665461078

ISBN: (纸本)9781665461078

Resilient operation of DC microgrids is seriously endangered by the unpredictable and volatile renewable generation, arbitrary load variations and unplanned disconnections from the utility grid. These instabilities can cause DC bus voltage fluctuations and significant power quality issues. Therefore, employing a robust, quick-responsive energy storage system with reduced lifetime degradation under frequent cycling operation, to maintain the power equilibrium, is a prominent solution in such circumstances. This study investigates the integration of a PI controlled supercapacitor energy storage (SCES) system into an islanded solar-wind DC microgrid. The proposed system is examined under normal and extreme climatic activity and under abrupt loading events. Simulations conducted in PSIM software demonstrate the efficacy of SCES employment, in terms of overall system stability and power quality. © 2022 IEEE.

关键词： Renewable energy resources

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Telemedicine System Based on Mixed Reality and Cognitive Speech Service Technologies 4

Telemedicine System Based on Mixed Reality and Cognitive Spe...

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4th IEEE Eurasia Conference on Biomedical engineering, Healthcare and Sustainability, ECBIOS 2022

作者： Lin, Pei-Jung Tsai, Bo-Chang Tsai, Yung-Wei Hungkuang University Dept. of Computer Science and Information Engineering Taichung Taiwan

ISBN: (数字)9781728195797

ISBN: (纸本)9781728195797

Because of the prevalence of COVID-19, medical capacity reduction, and inconvenience due to epidemic prevention policies, the development of telemedicine has been emphasized. An AI sensing device (i.e., Azure Kinect DK) with a mixed reality device (i.e., HoloLens 2) and window application are combined to design a system that completes the consultation process from a distance. This registering system uses an SQL server database, which reads the data of registered patients, selects the registration time and department on its own, and converts the data by PC/SC with an ISO7816 database. For the window application, the patient side screen and audio content for the window screen are captured through the lens and microphone array of Azure Kinect DK. HoloLens 2 is used as the main device on the doctor's side to design the window application for dictating case conversion and transmission, whereas the Unity engine is used to create mixed-reality scenes for viewing medical records and photos of patients. In the consultation part, doctor and patient devices use Dynamic 365 Assist to conduct video consultations via Microsoft Teams, transforming the traditional consultation form into a remote consultation service via the Internet. The consultation process records the consultation data by voice and then the prescription by Speech to Text and Azure cognitive voice service. It is expected to develop a hybrid-reality telemedicine system and combine metaverse-related technologies for more online interactive service functions. The future development goal is to integrate the virtual reality rehabilitation training system further to make the application of telemedicine more complete. © 2022 IEEE.

关键词： Mixed reality

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Enhanced ConvLSTM with Hierarchical Attention Scheme for Unusual Activity Detection in Classrooms or Laboratories

Enhanced ConvLSTM with Hierarchical Attention Scheme for Unu...

引用

IEEE Region 10 Symposium

作者： Ansh Mahendra Shrivas Dushyant Kumar Singh Rishabh Jain Shreyansh Singh Chandel Abhimanyu Sahu Computer Science and Engineering Dept. MNNIT Allahabad

ISBN: (数字)9798350364866

ISBN: (纸本)9798350364873

Surveillance camera systems have been essential in enhancing security protocols, serving as crucial tools for monitoring and deterring unauthorized or unusual activities across various settings. This paper addresses the significant vulnerability of educational spaces to numerous security threats, such as unauthorized access, theft, vandalism, and risks to the safety of students and staff. In an age where both physical and digital security are critically important, there is an increasing demand for proactive surveillance solutions that can quickly and accurately identify anomalies and potential security breaches. Deep learning techniques have demonstrated remarkable effectiveness in identifying patterns, anomalies, and subtle deviations in behavior within real-time video feeds. We have developed an enhanced ConvLSTM model incorporating spatial and temporal attention mechanisms to extract essential features from raw video footage, which are then processed by the backbone network. The architecture is designed to selectively focus on the most valuable features, thereby reducing computational complexity and enhancing the model's detection performance. The model is fine-tuned to detect and classify activities into usual and unusual categories. The proposed design is trained on videos from the UCF-Crime dataset and fine-tuned on a custom dataset created from YouTube videos, which includes videos of actions that are unusual or anomalous in educational environments. Our approach achieved approximately 99% accuracy on the UCF-Crime dataset, surpassing detection baselines (85.53% to 88.56%) and outperforming other architectures such as C3D, ConvLSTM, and CNN+RNN models with and without attention mechanisms. Evaluation on a custom YouTube dataset yielded a detection accuracy of 82.66%.

关键词： Video on demand Attention mechanisms Accuracy Computational modeling Surveillance computer architecture Feature extraction Security Web sites Object recognition

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Crowd Digital Twins: High-Level Requirements and Architecture

Crowd Digital Twins: High-Level Requirements and Architectur...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Roberto Casadei Giovanni Delnevo Roberto Girau Silvia Mirri Dept. of Computer Science and Engineering University of Bologna Italy

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Crowd management is the problem of dealing with physical crowds of humans, e.g., for safety reasons or to improve services through crowd-awareness. In previous work, the notion of a Crowd Digital Twin (CDT), namely the digital twin associated to physical gatherings of humans, was motivated and proposed as a fundamental component of crowd management systems. In this work, we better characterise the CDT notion, by investigating the functional and non-functional requirements that such a technical solution should satisfy. Then, we consider what are the fundamental components of CDT systems, and accordingly delineate a general architecture aimed at flexibly supporting the corresponding physical-digital thread.

关键词： Human-machine systems Computational modeling computer architecture Collective intelligence Digital twins Safety Proposals

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Grey wolf and Fuzzy logic based enhanced clustering approach for energy efficient operation of wireless sensor networks 7

Grey wolf and Fuzzy logic based enhanced clustering approach...

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7th International Conference on Communication and Electronics systems, ICCES 2022

作者： Rohilla, Hardik Himanshu Kumar, Harish Mehra, Pawan Singh Delhi Technological University Dept. of Computer Science and Engineering Delhi India

ISBN: (数字)9781665496346

ISBN: (纸本)9781665496346

A Wireless Sensor Network constitutes a collection of a large number of sensor nodes garrisoned in a given determined area so as to gather information and environmental data. They prove to be additionally beneficial in accumulating data from harsh environments where human intervention is scanty. The nodes are equipped with sensory, communication and computational capabilities which run on a battery which cannot be replaced effortlessly. Therefore, it becomes immensely important to work out an efficient, systematic and well-structured approach to inhibit power dissipation in the SNs so as to prolong the network lifetime, stability and service quality. This work puts forward a Grey Wolf optimisation(GWO) based procedure for cluster head selection and then employs fuzzy logic(FL) for efficient cluster development. The fitness parameters used in cluster head selection are remnant energy, standard intra cluster stretch, separation from sink, balancing factor and node degree. On the other hand, fitness of the CH and separation between node and a CH are the two parameters which serve as crisp inputs into the fuzzy inference system(FIS) in order to decide the cluster formation mechanism. On simulation of the proposed protocol and then comparing the results with corresponding protocols, our approach shows enhancements in the network performance metrics such as FND(First node death), residual energy, throughput. © 2022 IEEE.

关键词： Internet of things

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Analysis of Public Sentiment on Dhaka Metro Rail with Transformer Based Architectures 26

Analysis of Public Sentiment on Dhaka Metro Rail with Transf...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Jeba, Samiha Maisha Taharat Aurpa, Tanjim Siyam, Farshid Hossain Khan, Rajib Mansia, Faniyam Maria International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Bangladesh Bangabandhu Sheikh Mujibur Rahman Digital University Dept. of Educational Technology Bangladesh

ISBN: (纸本)9798350359015

Dhaka Metro Rail is a mesmerizing initiative of the Bangladesh Government that has brought comfort to people. This project got a huge appreciation from people for its continuous service. Mass people use the train for rambling, going to the workplace, etc. Again, for some unwanted circumstances, Dhaka Metro Rail gleaned negative criticism from various *** this research, we analyzed public sentiment on Dhaka Metro Rail from popular social media using recent NLP architectures like BERT, XLNet and Electra, where BERT outperformed than other with an accuracy of 91.11% in correctly identifying the sentiments of the public *** sentiment of a comment is identified into predefined categories, such as positive, negative, or neutral. © 2023 IEEE.

关键词： BERT ELECTRA Metro Rail Sentiment Analysis Vader XLNet

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Prediction of Heart Disease Using an Approach Based on Machine Learning 13

Prediction of Heart Disease Using an Approach Based on Machi...

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13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022

作者： Basak, Animesh Rahman, Md. Solaimanur Rahman, Mushfiqur Daffodil International University Dept. of Computer Science & Engineering Dhaka Bangladesh

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

Heart disease is one of the most consequential illnesses currently understood. Because of the widespread dissemination of information, numerous techniques and algorithms have been developed to better predict the prognosis of patients with cardiac disease. Using a dataset provided by Kaggle, this paper describes 13 crucial processes. This work was done by Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naïve Bayes, and Random Forest, which produced the most accurate findings with an accuracy of 93%. Everyone's comparative statement algorithms are also presented in the implementation section paper. This study also uses model validation techniques to design the most appropriate model to fit the current situation. © 2022 IEEE.

关键词： Forecasting

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